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The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Mark Rowland · Yunhao Tang · Clare Lyle · Remi Munos · Marc Bellemare · Will Dabney

Tue Jul 25 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #809

We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorithm, quantile temporal-difference learning (QTD), for this task. We reach the surprising conclusion that even if a practitioner has no interest in the return distribution beyond the mean, QTD (which learns predictions about the full distribution of returns) may offer performance superior to approaches such as classical TD learning, which predict only the mean return, even in the tabular setting.

Author Information

Mark Rowland (Google DeepMind)
Yunhao Tang (Google DeepMind)
Clare Lyle (University of Oxford)
Remi Munos (DeepMind)
Marc Bellemare (Google DeepMind)
Will Dabney (Google DeepMind)

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